Dzyaloshinskii–Moriya interaction gradient driven skyrmion based energy efficient leaky integrate fire neuron

The performance and energy efficiency of skyrmion-based spintronic devices can be greatly affected by the driving mechanism of skyrmion. Skyrmions in thin films can be efficiently displaced with spin-transfer torques (STT) and spin–orbit torques (SOT) using direct current. However, the utilization of current in these devices leads to the Joule heating issue, that compromises their non-volatility advantage in spintronic devices for neuromorphic computing. Therefore, it is necessary to investigate the alternative driving mechanisms for developing energy-efficient skyrmion based devices. In this work, skyrmion dynamics under Dzyaloshinskii–Moriya interaction (DMI) gradient is investigated. It is observed that the DMI gradient is an efficient way to drive the skyrmions with skyrmion Hall effect (SkHE) and a high longitudinal speed. Additionally, a leaky integrate-and-fire (LIF) neuron at room temperature is designed by employing DMI gradient that is energy efficient as it functions without the need for any current. This neuron utilizes a DMI gradient for integration on a trapezoidal-shaped nanotrack, achieving leaky functionality through the shape of the nanotrack itself. This design eliminates the need for external stimuli and additional hardware, resulting in low energy consumption of 91.363 fJ per spike. This opens a new alternative way to manipulate skyrmions for the development of energy-efficient skyrmion based devices.

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Publication Details

Journal
Journal of Magnetism and Magnetic Materials
Published
2024-11-28
DOI
https://doi.org/10.1016/j.jmmm.2024.172694
Citations
9
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
1.88

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article

Dzyaloshinskii–Moriya interaction gradient driven skyrmion based energy efficient leaky integrate fire neuron

Brajesh Kumar Kaushik, Shipra Saini, Sonal Shreya, Ravi Shankar Verma et al.
9 citations
Journal of Magnetism and Magnetic Materials
Advanced Memory and Neural Computing
1.88
article

Dzyaloshinskii–Moriya interaction gradient driven skyrmion based energy efficient leaky integrate fire neuron

Brajesh Kumar Kaushik, Shipra Saini, Sonal Shreya, Ravi Shankar Verma, Ravish Kumar Raj
article en
9 citations

Abstract

The performance and energy efficiency of skyrmion-based spintronic devices can be greatly affected by the driving mechanism of skyrmion. Skyrmions in thin films can be efficiently displaced with spin-transfer torques (STT) and spin–orbit torques (SOT) using direct current. However, the utilization of current in these devices leads to the Joule heating issue, that compromises their non-volatility advantage in spintronic devices for neuromorphic computing. Therefore, it is necessary to investigate the alternative driving mechanisms for developing energy-efficient skyrmion based devices. In this work, skyrmion dynamics under Dzyaloshinskii–Moriya interaction (DMI) gradient is investigated. It is observed that the DMI gradient is an efficient way to drive the skyrmions with skyrmion Hall effect (SkHE) and a high longitudinal speed. Additionally, a leaky integrate-and-fire (LIF) neuron at room temperature is designed by employing DMI gradient that is energy efficient as it functions without the need for any current. This neuron utilizes a DMI gradient for integration on a trapezoidal-shaped nanotrack, achieving leaky functionality through the shape of the nanotrack itself. This design eliminates the need for external stimuli and additional hardware, resulting in low energy consumption of 91.363 fJ per spike. This opens a new alternative way to manipulate skyrmions for the development of energy-efficient skyrmion based devices.

Journal of Magnetism and Magnetic MaterialsVol. 614
Indian Institute of Technology Roorkee (IN), Aarhus University (DK)
European Commission, Department of Science and Technology, Ministry of Science and Technology, India, Council of Scientific and Industrial Research, India, Ministry of Education, Ministry of Education, India, Science and Engineering Research Board
Affordable and clean energy
Openalex Percentile: Top 14%
Advanced Memory and Neural Computing
1.88
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